{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:16:02Z","timestamp":1767312962103,"version":"3.48.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032073426","type":"print"},{"value":"9783032073433","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-07343-3_26","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:13:49Z","timestamp":1767312829000},"page":"328-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SelCLR: Self-labeling with\u00a0Contrastive Learning and\u00a0Applications in\u00a0Machine Vision Systems"],"prefix":"10.1007","author":[{"given":"La Nguyen Gia","family":"Hy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duong Duc","family":"Tin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le Hong","family":"Trang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"26_CR1","unstructured":"Asano, Y.M., Rupprecht, C., Vedaldi, A.: Self-labelling via simultaneous clustering and representation learning. arXiv preprint arXiv:1911.05371 (2019)"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Bashkirova, D., et al.: Zerowaste dataset: towards deformable object segmentation in cluttered scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21147\u201321157 (2022)","DOI":"10.1109\/CVPR52688.2022.02047"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 132\u2013149 (2018)","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"26_CR4","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PmLR (2020)"},{"issue":"23","key":"26_CR5","doi-asserted-by":"publisher","first-page":"4723","DOI":"10.3390\/electronics12234723","volume":"12","author":"X Chen","year":"2023","unstructured":"Chen, X., et al.: Self-supervised clustering models based on byol network structure. Electronics 12(23), 4723 (2023)","journal-title":"Electronics"},{"key":"26_CR6","first-page":"21271","volume":"33","author":"JB Grill","year":"2020","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural. Inf. Process. Syst. 33, 21271\u201321284 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"He, Y., Wang, X., Shi, T.: Ddpm-moco: advancing industrial surface defect generation and detection with generative and contrastive learning. In: International Joint Conference on Artificial Intelligence, pp. 34\u201349. Springer, Cham (2024)","DOI":"10.1007\/978-981-97-9003-6_3"},{"key":"26_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110578","volume":"145","author":"X Hu","year":"2023","unstructured":"Hu, X., Yang, J., Jiang, F., Hussain, A., Dashtipour, K., Gogate, M.: Steel surface defect detection based on self-supervised contrastive representation learning with matching metric. Appl. Soft Comput. 145, 110578 (2023)","journal-title":"Appl. Soft Comput."},{"key":"26_CR11","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"26_CR12","unstructured":"Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks (2013). https:\/\/api.semanticscholar.org\/CorpusID:18507866"},{"key":"26_CR13","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","volume":"285","author":"K Song","year":"2013","unstructured":"Song, K., Yan, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 285, 858\u2013864 (2013)","journal-title":"Appl. Surf. Sci."},{"key":"26_CR14","doi-asserted-by":"publisher","unstructured":"Tin, D.D., Phuong, T.T., Trang, L.H.: Serf-p: a semi supervised approach via enhancing representative features and pseudo-labeling. In: Proceedings of the 2024 7th International Conference on Machine Vision and Applications, ICMVA 2024, pp. 41\u201347. Association for Computing Machinery, New York (2024). https:\/\/doi.org\/10.1145\/3653946.3653953","DOI":"10.1145\/3653946.3653953"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Wang, R., et al.: Deep contrastive one-class time series anomaly detection. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), pp. 694\u2013702. SIAM (2023)","DOI":"10.1137\/1.9781611977653.ch78"},{"key":"26_CR16","unstructured":"Wang, R., Hoppe, S., Monari, E., Huber, M.F.: Defect transfer GAN: diverse defect synthesis for data augmentation. arXiv preprint arXiv:2302.08366 (2023)"},{"issue":"1","key":"26_CR17","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s40537-023-00827-z","volume":"10","author":"M Zabin","year":"2023","unstructured":"Zabin, M., Kabir, A.N.B., Kabir, M.K., Choi, H.J., Uddin, J.: Contrastive self-supervised representation learning framework for metal surface defect detection. J. Big Data 10(1), 145 (2023)","journal-title":"J. Big Data"}],"container-title":["Lecture Notes in Computer Science","Advanced Concepts for Intelligent Vision Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07343-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:13:51Z","timestamp":1767312831000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07343-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032073426","9783032073433"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07343-3_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIVS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Concepts for Intelligent Vision Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acivs2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acivs2025.com","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}